1. Technical Field
This invention relates to the field of computer speech recognition and more particularly to a method and system for identifying excess noise in a computer system.
2. Description of the Related Art
Speech recognition, also referred to as speech-to-text, is the technology that enables a computer to transcribe spoken words into computer recognized text equivalents. Speech recognition is the process of converting an acoustic signal, captured by a transductive element, such as a microphone or a telephone, to a set of words. These words can be used for controlling computer functions, data entry, and word processing. The process can be initiated by speaking into a microphone. The microphone can capture the sound waves and can convert them into electrical impulses. Subsequently, a sound card can convert the electrical impulses from an analog acoustic audio signal into a digital audio signal.
Excess noise can adversely affect applications that require clean audio signals to properly function. Speech recognition software expects to “hear” only the speaker's voice and not extraneous noises. Of course, noises exist everywhere, intermittent and continual. Consequently, speech recognition software often attempts to assess the level of background noise at the outset. Having measured the level of background noise, the speech recognition system can subtract the measured noise from the speaker's acoustic signal.
Generally, background noise can include external background noise and internal system noise. Sources of external background noise can include regular home or office noises—conversation, the radio, traffic, telephones, the consumption of snack foods, and the crumpling of paper. In contrast, sources of internal system noise can include the electronic components on the sound card, network interface adapter or the modem, the system power supply, the microphone, the motors in a floppy, hard or CD-ROM drive, the printer engine, the scanner engine, and electrical activity stemming from the use of the keyboard, speakers or mouse. Though both external noise and internal noise can detrimentally effect the operation of a computer audio system, because external noise typically includes sounds within the realm of the human auditory system, only external noise can be easily identified by human users. In contrast, human users cannot aurally identify internal noise. Moreover, because internal noise is inherently unrecognizable to the human user, internal noise in most instances goes undetected by the human user.
In present systems, engineers recognize the multitude of potential sources of internal system noise. In the case of 32 and 64 bit sound cards, for instance, cross-talk can occur between the excess number of components placed on the sound card. Notably, many users of 32 and 64 bit sound cards have experienced problems with reducing internal system noise. Also, engineers note that sound chips permanently built-in on the main circuit board, resulting from space restrictions and cost cutting, often lead to a high level of background noise. Also, on-board chip sets are notorious for picking up electronic noise, particularly in the presence of excess disk activity.
Notwithstanding, where a human user can identify a noise generating internal component of a computer system, the user can remove the noisy component and the corresponding detrimental effect of the noisy component. Alternatively, in recognizing internal noise, a human user can avoid the use of the noisy system in its entirety. In either event, the identification of internal noise and the corresponding remedial action can translate into more productive audio application usage for the user.
At least one present speech recognition system has incorporated rudimentary noise detection. Yet, where included, present noise detection systems measure only a gross signal-to-noise ratio, taking into account the computer system as a whole. Present noise detection systems cannot isolate the source of internal noise. Moreover, present noise detection systems are unable to identify specific computer system component sources of the internal noise, and consequently are unable to recommend a remedy for the identified internal noise. Finally, present systems perform an incomplete analysis resulting in a potentially inaccurate diagnosis of internal noise level. Typically, present systems assess the background noise once, during a setup sequence, and use this measurement throughout future dictation. As a result, the user may be unaware of changes in the background noise level. For example, if in a tested system an internal hard disk drive is a source of internal noise, but remains inactive during noise detection, the noise detection system would incorrectly conclude a “quieter” computer system than the system would conclude were the hard disk drive active during the same test. Thus, there exists a need for a noise detection system capable of exercising each potential source of internal noise in a computer system. Only a thorough noise detection system can properly diagnose existing levels of internal noise in a computer system.